Polarimetric Synthetic Aperture Radar Image Classification by a Hybrid Method

Different methods proposed so far for accurate classification of land cover types in polarimetric synthetic aperture radar (SAR) image are data specific and no general method is available. A novel hybrid framework for this classification was developed in this work. A set of effective features derived from the coherence matrix of polarimetric SAR data was proposed. Constituents of the feature set are wavelet, texture, and nonlinear features. The proposed feature set has a strong discrimination power. A neural network was used as the classification engine in a unique way. By exploiting the speed of the conjugate gradient method and the convergence rate of the Levenberg-Marquardt method (near the optimal point), an overall speed up of the classification procedure was achieved. Principal component analysis (PCA) was used to shrink the dimension of the feature vector without sacrificing much of the classification accuracy. The proposed approach is compared with the maximum likelihood estimator (MLE) based on the complex Wishart distribution and the results show the superiority of the proposed method, with the average classification accuracy by the proposed method (95.4%) higher than that of the MLE (93.77%). Use of PCA to reduce the dimensionality of the feature vector helps reduce the memory requirements and computational cost, thereby enhancing the speed of the process.     

Passive Control Reinforced Concrete Frame Mechanism with High Strength Reinforcements and Its Potential Benefits Against Earthquakes

Introduction In most reinforced concrete (RC) structures, a large stiffness is needed in order to limit structural deforma- tion for service load conditions. In seismic resistant structures, however, the energy dissipation demands are imposed and inelasti…     

Affine Term Structure Model with Macroeconomic Factors: Do No‐Arbitrage Restriction and Macroeconomic Factors Imply Better Out‐of‐Sample Forecasts?

This study extends the affine dynamic Nelson–Siegel model for the inclusion of macroeconomic variables. Five macroeconomic variables are included in affine term structure model, derived under the arbitrage‐free restriction, to evaluate their role in the in‐sample fitting and out‐of‐sample forecasting of the term structure. We show that the relationship between the macroeconomic factors and yield data has an intuitive interpretation, and that there is interdependence between the yield and macroeconomic factors. Moreover, the macroeconomic factors significantly improve the forecast performance of the model. The affine Nelson–Siegel type models outperform the benchmark simple time series forecast models. The out‐of‐sample predictability of the affine Nelson–Siegel model with macroeconomic factors for the short horizon is superior to the simple affine yield model for all maturities, and for longer horizons the former is still compatible to the latter, particularly for medium and long maturities. Copyright © 2015 John Wiley & Sons, Ltd.     

最优同伦渐近法在带滑移的6常数Oldroyd流体模型中的应用

应用最优同伦渐近法（OHAM）研究了6常数Oldroydl流体的平面Couette流、平面Poiseuille流和平面Couette Poiseuille流这三个非线性问题.介绍了6常数Oldroyd流体的平面Couette流、平面Poiseuille流和平面Couette Poiseuille流的控制方程和边界条件,应用最优同伦渐近法对上述问题进行求解,并给出模拟仿真,根据仿真结果讨论了滑移参数对速度分布的影响.      

Neighborhood topological effect on grain topology-size relationship in three-dimensional polycrystalline microstructures

A new grain topology-size relationship in three-dimensional(3D)polycrystalline microstructures has recently been established by considering the effects of non-random first nearest neighbor grains.In this contribution,a generalized form for this relationship is presented by considering the interactions of kth(k=1,2,3…)nearest neighbor grains,and large scale Monte Carlo-Potts model simulation is used to investigate the general neighborhood topological effect on the topology-size relationship.The results show that,unlike their first nearest neighbors(k=1),the topological correlations of 3D grains with their kth layers(k 2)of nearest-neighbors may have trivial effect on the topology-size relationship.     

Algal biomass as a global source of transport fuels: Overview and development perspectives

As a result of the global fuel crisis of the early 1970 s,coupled with concerns for the environment,the use of biofuel has been on the increase in many regions throughout the world.At present,a total of approximately 30 billion(30×109) liters of biofuel are utilized worldwide annually,although most countries rely hugely on the first generation biofuel.The limitations of the first and second generation biofuel gave rise to current interest in algae as a promising alternative to these conventional biofuel sources.Algal biomass could provide a lion’s share of the global transport fuel requirements in future.The present review highlights some important developments in,and potentials of algaculture as a major biomass resource of the future.However,the major constraint to commercial-scale algae farming for energy production is the cost factor,which must be addressed adequately before its potentials can be harnessed.     

Term Structure Forecasting of Government Bond Yields with Latent and Macroeconomic Factors: Do Macroeconomic Factors Imply Better Out‐of‐Sample Forecasts?

This study empirically examines the role of macroeconomic and stock market variables in the dynamic Nelson–Siegel framework with the purpose of fitting and forecasting the term structure of interest rate on the Japanese government bond market. The Nelson–Siegel type models in state‐space framework considerably outperform the benchmark simple time series forecast models such as an AR(1) and a random walk. The yields‐macro model incorporating macroeconomic factors leads to a better in‐sample fit of the term structure than the yields‐only model. The out‐of‐sample predictability of the former for short‐horizon forecasts is superior to the latter for all maturities examined in this study, and for longer horizons the former is still compatible to the latter. Inclusion of macroeconomic factors can dramatically reduce the autocorrelation of forecast errors, which has been a common phenomenon of statistical analysis in previous term structure models. Copyright © 2013 John Wiley & Sons, Ltd.     

硫酸中香蕉皮还原低品位锰矿的浸出行为及动力学模型

Manganese was leached from a low-grade manganese ore (LGMO) using banana peel as the reductant in a dilute sulfuric acid medium. The effects of banana peel amount, H₂SO₄ concentration, reaction temperature, and time on Mn leaching from the complex LGMO were studied. A leaching efficiency of ~98% was achieved at a leaching time of 2 h, banana peel amount of 4 g, leaching temperature of 120°C, manganese ore amount of 5 g, and sulfuric acid concentration of 15vol%. The phase, microstructural, and chemical analyses of LGMO samples before and after the leaching process confirmed the successful leaching of manganese. Furthermore, the leaching process followed the shrinking core model and the leaching rate was controlled by a surface chemical reaction (1 ? (1 ? x)1/3 = kt) mechanism with an apparent activation energy of 40.19 kJ·mol?1.